SmartRx - overview

The SmartRx workflow leverages docking, molecular dynamics, and free energy calculations to enable the faster identification and optimization of potent and novel drug leads. Molecular dynamics calculations can guide the design of novel compounds by targeting conformational states not evident in the static crystal structures and represent the induced fit effects seen with new active compounds. Although multiple crystal structures are often available for a given target in complex with a variety of small molecules, and structures of other targets from the same family also offer insights, accessing the full ensemble of available protein conformational states requires the use of molecular dynamics calculations.

When the molecular dynamics calculations are carried out in a free energy paradigm, candidate small molecules can also be ranked to prioritize them for synthesis. Small molecule lead optimization typically requires synthesis of hundreds of molecules to achieve target potency, selectivity, and other properties, so faster optimization of on-target potency allows more time to explore other properties. IBM’s significant hardware resources support high throughput for these compute-intensive calculations.

The SmartRx team has completed multiple successful client engagement using docking and molecular dynamics software to design small molecules for food and beverage companies. The team’s technical expertise is a key element of the successful application of these complex techniques. Further, the SmartRx team lead has 20 years of pharmaceutical industry experience at Novartis and Merck and Co., Inc.  The team has expertise with a wide variety of targets including but not limited to kinases, metalloenzymes, other enzymes, nuclear hormone receptors, and GPCRs.

Ongoing development of the SmartRx workflow focuses on the incorporation of deep learning AI techniques to further exploit the protein structure information.

Team Members

  • Wendy Cornell
  • Sugato Bagchi
  • Tien Huynh
  • Seung gu Kang
  • Maeve Kavanagh
  • Joe Morrone
  • Aarushi Parashar
  • Jeff Weber